Faster horn diagnosis - a performance comparison of abductive reasoning algorithms

Abstract

Abductive inference derives explanations for encountered anomalies and thus embodies a natural approach for diagnostic reasoning. Yet its computational complexity, which is inherent to the expressiveness of the underlying theory, remains a disadvantage. Even when restricting the representation to Horn formulae the problem is NP-complete. Hence, finding procedures that can efficiently solve abductive diagnosis problems is of particular interest from a research as well as practical point of view. In this paper, we aim at providing guidance on choosing an algorithm or tool when confronted with the issue of computing explanations in propositional logic-based abduction. Our focus lies on Horn representations, which provide a suitable language to describe most diagnostic scenarios. We illustrate abduction via two contrasting problem formulations: direct proof methods and conflict-driven techniques. While the former is based on determining logical consequences, the later searches for suitable refutations involving possible causes. To reveal runtime performance trends we conducted a case study, in which we compared publicly available general purpose tools, established Horn reasoning engines, as well as new variations of known methods as a means for abduction. Keywords Abductive reasoning · Model-based diagnosis · Abductive diagnosisApplied Intelligence (2020) 50:1558-15 2 7 Published online: 2 2020 January 8 A = Damaged pump, Broken filter, Cooler leaks, Cooler cracks, reduced pressure, poor lubrication, overheating Hyp = Damaged pump, Broken filter, Cooler leaks, Cooler cracks Th = ⎧ ⎨ ⎩ Damaged pump → reduced pressure, reduced pressure → poor lubrication, Broken filter ∧ overheating → poor lubrication, Cooler leaks ∧ Cooler cracks → overheating ⎫ ⎬ ⎭ R. Koitz-Hristov and F. Wotawa 1560